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Data Driven Growth (Montreal 2015)
- 1. Data Driven Growth 1 John Egan Engineering Manager - Engagement Team @ Pinterest
- 2. 2 • Retail shopping rewards app • Led Growth Engineering team • Grew from 1MM to 8MM users in 3 years • Acquired for $200MM • Visual discovery & bookmarking app • Eng manager for Engagement • Over 100 million MAUs • Engagement experiments have added millions of WAUs
- 10. Funnels 10
- 11. 11 2013
- 12. 12 2014
- 13. 13 2015
- 14. Activation 14
- 15. Retained MAUs • Percentage of new signups that are still using the app a month later • Figure out what a good retention number is for your industry 15
- 16. 1d7s • Percentage of new signups that use the app 1 or more times in the week following signup • Quicker to run experiments with • At Pinterest this is highly correlated with user’s long-term retention 16
- 17. 17
- 18. 18
- 19. Cohort Heatmap 19 d0 User’s Signup Date d30 d60 DaysSinceJoining
- 20. Cohort Heatmap 20Source: Placeholder Numbers
- 21. Cohort Heatmap 21Source: Placeholder Numbers
- 22. Engagement 22
- 23. One Key Engagement Metric Medium: Total Time Spent Reading (TTR) Twitter: MAUs with 7+ visits a month (7d28s) Uber: Weekly Trips Facebook: WAUs with 6+ visits a week (6L7s) Pinterest: Weekly Active Repiners or Clickers (WARCs) 23
- 24. Finding Your Key Engagement Metric 1) What are the actions a user has to take to get value from your product? 2) What is the frequency someone need to use your product 24
- 25. User States Core: Active multiple times a week Casual: Active once or twice a week Marginal: Active a couple times a month New: Joined in the past 28 days Dormant: Not active for 28 days Resurrected: Was dormant, but became active again in the past 28 days 25
- 27. Measuring Effect on Engagement 27
- 28. *AU Ratios 28
- 29. 29 DAU MAU DAU/MAU ratio Knighthood 30,014 389,583 7.70% RockYou Pets 50,482 582,720 8.66% Tetris Friends 100,404 841,223 11.94% Superpoke Pets 299,505 2,296,175 13.04% Mobsters 539,012 3,431,063 15.71% Vampire Wars 687,067 3,794,869 18.11% Country Story 1,647,763 8,097,356 20.35% Fish World 2,084,358 8,581,587 24.29% Pet Society 5,046,347 20,480,397 24.64% Mafia Wars 6,473,504 25,431,622 25.45% Restaurant City 5,033,468 17,250,419 29.18% Cafe World 7,409,711 22,062,364 33.59% Source: Social Times & Nabeel Hyatt
- 31. Marketing Pros: • Coverage. Can reach the entire user base Cons: • Not personalized, lower open rates • User’s have much lower tolerance compared to emails 31
- 32. Transactional Pros: • More personal than marketing notifications Cons: • Users need activity to generate notifications • Need mechanisms to rate limit 32
- 33. Recommendations Pros: • Coverage. Can reach most users • Personalized to the user Cons: • Expensive and time consuming to build out recommendation engine • Quality may not be good if user has low amount of activity 33 2:50 PM 100%2:50 PM 100%
- 34. Measuring Engagement for Emails/Push • Positive measures of quality - Must lift key engagement metric - Minimum required CTR rates • Negative measures of quality - App deletions - Spam reports - Unsubscribes 34
- 36. All Users 36
- 37. Marginal Users (~1 month) 37
- 38. Core Users (multiple times a week) 38
- 41. Initial Traction
- 42. Do Things That Don’t Scale 42
- 43. Growth Stage
- 45. Wrap Up • Use funnels for analyzing acquisition flows • 1d7s & cohort heatmaps to analyze on activation • Use user states to understand how engaged users are • Make sure emails/notifications for true engagement • Use segmentation for experiments & metrics in general • Product comes first • Do things that don’t scale • Prioritize projects based on return on investment 45
- 46. John Egan Growth Blog: jwegan.com Email: me@jwegan.com Twitter: @jwegan_com Pinterest: pinterest.com/jwegan 46